1. Load your neuron in CATMAID and check the root of the neuron is at the soma and you have tagged all the nodes you want to prune from.
#Load all the necessary libraries
library(catmaid)
library(elmr)
skid <- 534333
n <- read.neuron.catmaid(skid) #aSPg neuron
Cut neuron at specified tagged nodes, in this case “AJES_cut_x”
#Get the nodes for the tags with "AJES_cut" in them
cut_nodes <- n$tags[grepl("AJES_cut", names(n$tags))]
#Reorder to AJES_cut_1, AJES_cut_2, AJES_cut_3, AJES_cut_4
cut_nodes <- cut_nodes[sort(names(cut_nodes))]
#Get X,Y,Z coords of cut_nodes to plot in 3d
n$d$X[n$d$PointNo %in% cut_nodes]
[1] 631533 632757 631631 629468
n$d$Y[n$d$PointNo %in% cut_nodes]
[1] 150459 149966 162402 158085
n$d$Z[n$d$PointNo %in% cut_nodes]
[1] 80000 81520 89720 91400
nview3d("frontal")
plot3d(n, soma = T, col = "black", WithConnectors = T)
col <- rainbow(length(cut_nodes))
sapply(1:4, function(x) points3d(n$d$X[n$d$PointNo %in% cut_nodes[x]],
n$d$Y[n$d$PointNo %in% cut_nodes[x]],
n$d$Z[n$d$PointNo %in% cut_nodes[x]],
size = 10,
col = col[x],
pch = 16)) #pch = 16 plots circles, use ?points to find other shapes plotted with the pch argument.
[1] 76 77 78 79
legend3d("topright", legend = names(cut_nodes), fill = col)
rglwidget()
#Lets start by cutting the neuron distal to AJES_cut_1.
dist <- distal_to(n, node.pointno = cut_nodes[[1]]) #Return indices of points in a neuron distal to a given node
#Subset our neuron by dist indices
neuron.distal.points <- n$d[dist,]
Plot points distal to cut point 1.
clear3d()
legend3d()
Error in as.graphicsAnnot(legend) :
argument "legend" is missing, with no default
nview3d("frontal")
plot3d(n, col = "gray23", soma = T)
points3d(neuron.distal.points[,c('X','Y','Z')], col = "deepskyblue")
rglwidget()
#That's great, but we are only interested in the axons on the left hemisphere. Not the ones on the right too.
#Lets remove everything distal to AJES_cut_2 from everything distal to AJES_cut1
#Lets start by cutting the neuron distal to AJES_cut_1.
dist1 <- distal_to(n, node.pointno = cut_nodes[[1]])
dist2 <- distal_to(n, node.pointno = cut_nodes[[2]])
#Subset our neuron by dist indices
neuron.distal.points <- n$d[setdiff(dist1, dist2),]
clear3d()
nview3d("frontal")
plot3d(n, col = "gray23", soma = T)
points3d(neuron.distal.points[,c('X','Y','Z')], col = "deepskyblue")
rglwidget()
# Or there's the option of including points distal to two cuts...
dist3 <- distal_to(n, node.pointno = cut_nodes[[3]])
dist4 <- distal_to(n, node.pointno = cut_nodes[[4]])
neuron.distal.points <- n$d[c(dist3, dist4),]
clear3d()
nview3d("frontal")
plot3d(n, col = "gray23", soma = T)
points3d(neuron.distal.points[,c('X','Y','Z')], col = "deepskyblue")
rglwidget()
Connectivity matrix of split neuron
#For our example lets just use the first cut point, and generate a connectivity matrix for the distal neuron
neuron.distal.points <- n$d[dist,]
#Generate connectivity matrix for neuron we are interested in
con_mat <- catmaid_get_connectors_between(post_skids = skid)
#subset connectivity matrix to only contain points in neuron.distal.points
neuron.distal.points #neuron.distal.points contains the following:
con_mat #and the connectivity matrix is a dataframe containing this informtation
#depending on whether you specified pre or post_skids in the catmaid_get_connectors_between we can subset con_mat by post_node_id or pre_node_id. In this example we are using post_skids and post_node_id to look at inputs to our neuron.
#subset con_mat by nodeid/PointNo from neuron.distal.points
con_mat$post_node_id %in% neuron.distal.points$PointNo #returns a logical matrix, TRUE values are connections in our distal branch.
[1] TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[19] FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[55] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[91] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[109] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[127] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[145] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[163] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[181] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[199] FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
[217] FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE
con_mat[con_mat$post_node_id %in% neuron.distal.points$PointNo,] #don't forget the "," as con_mat is a dataframe.
dist_con_mat <- con_mat[con_mat$post_node_id %in% neuron.distal.points$PointNo,]
#We can even plot all of these connections to double check the connections are in the right place.
clear3d()
plot3d(n, soma = T, col = "grey")
points3d(neuron.distal.points[,c('X','Y','Z')], col = "deepskyblue")
points3d(dist_con_mat[,c("connector_x", "connector_y", "connector_z")], col = "green", size = 5)
rglwidget()
So you want to prune a neuron object instead?!
#Great! There's a handy function subset() in the nat package.
dist.n <- subset(n, dist)
names(dist.n)
[1] "NumPoints" "StartPoint" "BranchPoints" "EndPoints" "nTrees" "NumSegs" "SegList"
[8] "d"
names(n)
[1] "NumPoints" "StartPoint" "BranchPoints" "EndPoints" "nTrees" "NumSegs" "SegList"
[8] "d" "skid" "connectors" "tags" NA NA "url"
[15] "headers"
#note how there is less information in the dist.n neuron object. It loosess connectivity information.
Task/Challenge: Try generate a connectivity matrix for the other cut points
2. Use the following code to generate a sampling spreadsheet.
#gives random ordering of pre-synaptic profiles
node = 29496430
skid = 6065216
#function to return the incoming pre-synaptic nodes for the selected skid proximal of the provided cut point
incoming_connections <- function(skid, node) {
neuron <- read.neuron.catmaid(skid)
dist <- distal_to(neuron, node.pointno = node)
neuron.distal.points <- neuron$d[-dist,]
#debug- graph to check selected distal points are in correct region
nopen3d()
plot3d(neuron, col = "gray23")
points3d(neuron.distal.points[,c('X','Y','Z')], col = "deepskyblue")
all_connectors <- catmaid_get_connectors_between(post_skids = skid)
connectors = all_connectors[all_connectors$post_node_id %in% neuron.distal.points$PointNo,]
#debug - graph to check all selected connectors in correct region
nopen3d()
plot3d(neuron, col = "black")
points3d(connectors[,c('connector_x','connector_y','connector_z')])
return(connectors)
}
#get incoming synapses
n_incoming <- incoming_connections(skid = skid, node = node)
#randomise outgoing connectors for tracing downstream partners
perm = n_incoming[sample(nrow(n_incoming)),]
#debug -
nopen3d()
glX
14
neuron <- read.neuron.catmaid(skid)
plot3d(neuron)
points3d(n_incoming[,c('post_node_x','post_node_y','post_node_z')], col='blue')
points3d(perm[,c('post_node_x','post_node_y','post_node_z')], col = 'green', size = 8)
#—URL generator—
connector_URL <- function(dfrow){
base = "https://neuropil.janelia.org/tracing/fafb/v14/"
catmaid_url = paste0(base, "?pid=1")
catmaid_url = paste0(catmaid_url, "&zp=", dfrow[,"pre_node_z"])
catmaid_url = paste0(catmaid_url, "&yp=", dfrow[,"pre_node_y"])
catmaid_url = paste0(catmaid_url, "&xp=", dfrow[,"pre_node_x"])
catmaid_url = paste0(catmaid_url, "&tool=tracingtool")
catmaid_url = paste0(catmaid_url, "&active_skeleton_id=", dfrow[,'pre_skid'])
catmaid_url = paste0(catmaid_url, "&active_node_id=", dfrow[,"pre_node_id"])
catmaid_url = paste0(catmaid_url, "&sid0=5&s0=0")
invisible(catmaid_url)
}
#———
#generate URLs for each row
perm$URL = character(nrow(perm))
perm[,"URL"] = sapply(1:nrow(perm), function(x) perm[x, "URL"] = connector_URL(perm[x,]))
#write out as CSV to save in Google docs
write.csv(perm, file = 'test.csv')
library(googlesheets)
gs_ls()
Adding .httr-oauth to .gitignore
Waiting for authentication in browser...
Press Esc/Ctrl + C to abort
NA
---
title: "Splitting neurons into fragments for sampling, aSPg example"
output: html_notebook
---

# 1. Load your neuron in CATMAID and check the root of the neuron is at the soma and you have tagged all the nodes you want to prune from.

```{r}
#Load all the necessary libraries
library(catmaid)
library(elmr)
```

```{r}
skid <- 534333
n <- read.neuron.catmaid(skid) #aSPg neuron
```

#Cut neuron at specified tagged nodes, in this case "AJES_cut_x"
```{r}
#Get the nodes for the tags with "AJES_cut" in them
cut_nodes <- n$tags[grepl("AJES_cut", names(n$tags))]
#Reorder to AJES_cut_1, AJES_cut_2, AJES_cut_3, AJES_cut_4
cut_nodes <- cut_nodes[sort(names(cut_nodes))]
#Get X,Y,Z coords of cut_nodes to plot in 3d
n$d$X[n$d$PointNo %in% cut_nodes]
n$d$Y[n$d$PointNo %in% cut_nodes]
n$d$Z[n$d$PointNo %in% cut_nodes]
```
```{r}
nview3d("frontal")
plot3d(n, soma = T, col = "black", WithConnectors = T)
col <- rainbow(length(cut_nodes))
sapply(1:4, function(x) points3d(n$d$X[n$d$PointNo %in% cut_nodes[x]], 
                                 n$d$Y[n$d$PointNo %in% cut_nodes[x]],
                                 n$d$Z[n$d$PointNo %in% cut_nodes[x]],
                                 size = 10,
                                 col = col[x],
                                 pch = 16)) #pch = 16 plots circles, use ?points to find other shapes plotted with the pch argument.
legend3d("topright", legend = names(cut_nodes), fill = col)
rglwidget()
```


```{r}
#Lets start by cutting the neuron distal to AJES_cut_1.
dist <- distal_to(n, node.pointno = cut_nodes[[1]]) #Return indices of points in a neuron distal to a given node
#Subset our neuron by dist indices
neuron.distal.points <- n$d[dist,]
```
#Plot points distal to cut point 1.
```{r}
clear3d()
legend3d()
nview3d("frontal")
plot3d(n, col = "gray23", soma = T)
points3d(neuron.distal.points[,c('X','Y','Z')], col = "deepskyblue")
rglwidget()
```

```{r}
#That's great, but we are only interested in the axons on the left hemisphere. Not the ones on the right too.
#Lets remove everything distal to AJES_cut_2 from everything distal to AJES_cut1
#Lets start by cutting the neuron distal to AJES_cut_1.
dist1 <- distal_to(n, node.pointno = cut_nodes[[1]])
dist2 <- distal_to(n, node.pointno = cut_nodes[[2]])

#Subset our neuron by dist indices
neuron.distal.points <- n$d[setdiff(dist1, dist2),]

```

```{r}
clear3d()
nview3d("frontal")
plot3d(n, col = "gray23", soma = T)
points3d(neuron.distal.points[,c('X','Y','Z')], col = "deepskyblue")
rglwidget()

```

```{r}
# Or there's the option of including points distal to two cuts...
dist3 <- distal_to(n, node.pointno = cut_nodes[[3]])
dist4 <- distal_to(n, node.pointno = cut_nodes[[4]])
neuron.distal.points <- n$d[c(dist3, dist4),]
```

```{r}
clear3d()
nview3d("frontal")
plot3d(n, col = "gray23", soma = T)
points3d(neuron.distal.points[,c('X','Y','Z')], col = "deepskyblue")
rglwidget()
```

#Connectivity matrix of split neuron
```{r}
#For our example lets just use the first cut point, and generate a connectivity matrix for the distal neuron
neuron.distal.points <- n$d[dist,]
#Generate connectivity matrix for neuron we are interested in
con_mat <- catmaid_get_connectors_between(post_skids = skid)
#subset connectivity matrix to only contain points in neuron.distal.points
neuron.distal.points #neuron.distal.points contains the following:
```
```{r}
con_mat #and the connectivity matrix is a dataframe containing this informtation
```
```{r}
#depending on whether you specified pre or post_skids in the catmaid_get_connectors_between we can subset con_mat by post_node_id or pre_node_id. In this example we are using post_skids and post_node_id to look at inputs to our neuron.

#subset con_mat by nodeid/PointNo from neuron.distal.points
con_mat$post_node_id %in% neuron.distal.points$PointNo  #returns a logical matrix, TRUE values are connections in our distal branch.
con_mat[con_mat$post_node_id %in% neuron.distal.points$PointNo,] #don't forget the "," as con_mat is a dataframe.
dist_con_mat <- con_mat[con_mat$post_node_id %in% neuron.distal.points$PointNo,]
```
```{r}
#We can even plot all of these connections to double check the connections are in the right place.
clear3d()
plot3d(n, soma = T, col = "grey")
points3d(neuron.distal.points[,c('X','Y','Z')], col = "deepskyblue")
points3d(dist_con_mat[,c("connector_x", "connector_y", "connector_z")], col = "green", size = 5)
rglwidget()
```

#So you want to prune a neuron object instead?!
```{r}
#Great! There's a handy function subset() in the nat package.
dist.n <- subset(n, dist)
names(dist.n)
names(n)
#note how there is less information in the dist.n neuron object. It loosess connectivity information.
```

```{r}
clear3d()
plot3d(dist.n, WithNodes = F, col = "blue", soma = T)
plot3d(n, col = "black", soma = T)
rglwidget()
```


#Task/Challenge: Try generate a connectivity matrix for the other cut points
#2. Use the following code to generate a sampling spreadsheet.

```{r}
#gives random ordering of pre-synaptic profiles
node = 29496430
skid = 6065216
#function to return the incoming pre-synaptic nodes for the selected skid proximal of the provided cut point

incoming_connections <- function(skid, node) {
  neuron <- read.neuron.catmaid(skid)
  dist <- distal_to(neuron, node.pointno = node)
  neuron.distal.points <- neuron$d[-dist,]
  
  
  
  #debug- graph to check selected distal points are in correct region
  nopen3d()
  plot3d(neuron, col = "gray23")
  points3d(neuron.distal.points[,c('X','Y','Z')], col = "deepskyblue")
  
  all_connectors <- catmaid_get_connectors_between(post_skids = skid)
  connectors = all_connectors[all_connectors$post_node_id %in% neuron.distal.points$PointNo,]
  #debug - graph to check all selected connectors in correct region
  nopen3d()
  plot3d(neuron, col = "black")
  points3d(connectors[,c('connector_x','connector_y','connector_z')])
  
  return(connectors)
}

#get incoming synapses 
n_incoming <- incoming_connections(skid = skid, node = node)
#randomise outgoing connectors for tracing downstream partners
perm = n_incoming[sample(nrow(n_incoming)),]
#debug - 
nopen3d()
neuron <- read.neuron.catmaid(skid)
plot3d(neuron)
points3d(n_incoming[,c('post_node_x','post_node_y','post_node_z')], col='blue')
points3d(perm[,c('post_node_x','post_node_y','post_node_z')], col = 'green', size = 8)
#—URL generator—
connector_URL <- function(dfrow){
  base = "https://neuropil.janelia.org/tracing/fafb/v14/"
  catmaid_url = paste0(base, "?pid=1")
  catmaid_url = paste0(catmaid_url, "&zp=", dfrow[,"pre_node_z"])
  catmaid_url = paste0(catmaid_url, "&yp=", dfrow[,"pre_node_y"])
  catmaid_url = paste0(catmaid_url, "&xp=", dfrow[,"pre_node_x"])
  catmaid_url = paste0(catmaid_url, "&tool=tracingtool")
  catmaid_url = paste0(catmaid_url, "&active_skeleton_id=", dfrow[,'pre_skid'])
  catmaid_url = paste0(catmaid_url, "&active_node_id=", dfrow[,"pre_node_id"])
  catmaid_url = paste0(catmaid_url, "&sid0=5&s0=0")
  
  invisible(catmaid_url)
}
#———
#generate URLs for each row
perm$URL = character(nrow(perm))
perm[,"URL"] = sapply(1:nrow(perm), function(x) perm[x, "URL"] = connector_URL(perm[x,]))
#write out as CSV to save in Google docs
write.csv(perm, file = 'test.csv')

library(googlesheets)
gs_ls()
y5 <- gs_title("TITLE HERE") #Insert your title here
gs_edit_cells(y5, ws = 1, input = perm$pre_skid, anchor = "A3", byrow = FALSE, col_names = FALSE)
gs_edit_cells(y5, ws = 1, input = perm$post_skid, anchor = "B3", byrow = FALSE, col_names = FALSE)
gs_edit_cells(y5, ws = 1, input = perm$connector_id, anchor = "C3", byrow = FALSE, col_names = FALSE)
gs_edit_cells(y5, ws = 1, input = perm$post_node_id, anchor = "D3", byrow = FALSE, col_names = FALSE)
gs_edit_cells(y5, ws = 1, input = perm$URL, anchor = "E3", byrow = FALSE, col_names = FALSE)

```


